Interlinking Heterogeneous Data for Smart Energy Systems
Citation:
Fabrizio Orlandi, Alan Meehan, Murhaf Hossari, Soumyabrata Dev, Declan O'Sullivan, Tarek Alskaif, Interlinking Heterogeneous Data for Smart Energy Systems, IEEE International Conference on Smart Energy Systems and Technologies (SEST), Spain, 10th September, Arxiv.org; Cornell University, 2019, 20-26Abstract:
Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis- A nd are designed on-specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
13/RC/2106
Author's Homepage:
http://people.tcd.ie/osulldpsDescription:
PUBLISHEDSpain
Author: O'Sullivan, Declan
Other Titles:
IEEE International Conference on Smart Energy Systems and Technologies (SEST)Publisher:
Arxiv.org; Cornell UniversityType of material:
Conference PaperCollections
Series/Report no:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Availability:
Full text availableKeywords:
Smart energy systems, Ontologies, Meteorology, Resource description framework, Photovoltaic systems ,, Semantics, Linked dataSubject (TCD):
Digital Engagement , Knowledge and data engineeringDOI:
10.1109/SEST.2019.8849055Metadata
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